Two Production Bugs, One Pipeline: Deadlock, Collision, and the Path to a Final Docker Image

Introduction

In the world of distributed GPU proving for Filecoin, where cryptographic proofs must be computed across heterogeneous hardware under concurrent load, production bugs rarely arrive alone. This article examines a single segment of an opencode coding session—spanning dozens of messages across two chunks—that resolved two critical production bugs in the ProofShare system: a deadlock caused by an infinite HTTP 429 retry loop, and a job ID collision that caused concurrent proof challenges to produce scrambled, invalid proofs. Between these two fixes, the assistant navigated Docker build cache traps, hot-swapped binaries on a remote GPU host, audited every caller of a vulnerable function, consolidated all changes into a single amended commit, and pushed the final Docker image that would serve as the canonical deployment for every future GPU proving instance.

The story is a masterclass in distributed systems debugging, revealing how systematic hypothesis elimination, controlled experimentation, and operational persistence combine to fix bugs that span multiple languages (Go, Rust), runtime environments (Docker, vast.ai), and abstraction layers (HTTP clients, GPU proving pipelines, database queues). It also demonstrates the critical importance of the deployment pipeline itself—how the very tools used to deliver fixes can silently betray those fixes, and how careful verification at every step is the only defense against phantom deployments.

Part I: The Deadlock — When a 429 Retry Loop Starves the Polling Cycle

The Symptom: Stuck Tasks and an Empty Queue

The first bug manifested as a complete stall in the ProofShare proof request pipeline. PSProve tasks were not running, the proofshare_queue was empty, and the remote proof service was returning HTTP 429 (Too Many Requests) responses. The system appeared to be rate-limited into a standstill.

The assistant traced the causal chain through three files:

  1. lib/proofsvc/provictl.go — The CreateWorkAsk function had an infinite retry loop for HTTP 429 responses, with exponential backoff capped at 5 minutes and no overall timeout. While stuck in this loop, the function could never return to the caller.
  2. tasks/proofshare/task_request.go — The Do() function ran a polling loop that: (a) polled the remote service for matched work, (b) inserted matched work into the local proofshare_queue, and (c) called CreateWorkAsk to solicit more work. Because CreateWorkAsk blocked indefinitely on 429, step (c) never completed, so the loop never returned to step (a). Matched work that had been assigned to existing asks went undiscovered.
  3. tasks/proofshare/task_prove.go — The queue cleanup routine used DELETE for orphaned rows, destroying work records that could have been re-assigned to new harmony tasks. The deadlock was self-reinforcing: the retry loop blocked the poll loop, which prevented work from being inserted into the queue, which meant PSProve tasks never completed, which meant the service never freed ask slots, which meant the 429 condition never cleared. A perfect distributed systems trap.

The Fix: Breaking the Loop at the Right Layer

The assistant's fix, refined through collaboration with the user, involved several coordinated changes:

Sentinel error in CreateWorkAsk: The function was modified to return a new ErrTooManyRequests sentinel immediately on HTTP 429, instead of retrying forever. The retry responsibility moved from the low-level HTTP client to the higher-level polling loop, which had visibility into whether the system was making progress.

Progress-based backoff in the polling loop: The Do() function now tracks madeProgress (new work inserted or asks created) and rateLimited. When no progress occurs and the service returns 429, the poll interval doubles up to 2 minutes. When progress resumes, the interval resets to 3 seconds. This prevents tight-looping against the service while ensuring rapid recovery when conditions improve.

Scoped dedup query: The SELECT service_id FROM proofshare_queue was changed to WHERE submit_done = FALSE, since submitted rows can never match new asks. This prevents unbounded table scans.

Orphan reset instead of delete: The DELETE for orphaned rows was changed to UPDATE ... SET compute_task_id = NULL, preserving fetched work so it can be re-assigned rather than lost.

Completed row purge: A routine was added to DELETE FROM proofshare_queue WHERE submit_done = TRUE AND obtained_at < NOW() - INTERVAL '2 days', preventing unbounded table growth.

The assistant verified the build with go build and go vet. The deadlock was broken.

Part II: The Job ID Collision — When All Partitions Are Invalid

The Symptom: Benchmark Passes, Production Fails

The user confirmed that the deadlock fix worked: "Proof requesting fixed now." But immediately followed with devastating news: the CuZK GPU proving engine was still producing invalid proofs. All ten partitions of every PoRep proof were failing verification. The benchmark script (benchmark.sh) passed inside the Docker container, but the production pipeline (run.sh + Curio) generated nothing but garbage.

The logs showed a critical clue: "assembler not found, discarding partition result job_id=ps-porep-1000-1 partition=0". The CuZK engine's partition assembler, which collects GPU-proved partition results and combines them into a final proof, could not find the assembler for this job ID. Partition results were being discarded.

The assistant initially chased binary mismatches—the production host was running a binary built via Dockerfile.cuzk-rebuild, while benchmarks ran inside a full Dockerfile.cuzk container. The hashes differed. But even after extracting the "good" binary from the container and deploying it, all ten partitions still failed. The binary wasn't the problem.

The Smoking Gun: A Controlled Experiment

The user delivered the breakthrough. The logs included a Rust panic:

thread 'tokio-runtime-worker' panicked at cuzk-core/src/pipeline.rs:1910:9:
partition 0 already inserted

This was the smoking gun. The partition assembler, which uses a HashMap keyed on job_id, was trying to insert partition 0 into a slot that already contained partition 0. Two different proof jobs were sharing the same assembler key.

The user then ran a controlled experiment: set proofshare_max_tasks=1, limiting the system to a single concurrent proofshare task. Result: "Correct proof!" With no concurrency, there could be no collision. The user's hypothesis was precise: "job-id is derived from minerid-sectorid? Challenge sectors are all the same sector."

Indeed, the RequestId was formatted as fmt.Sprintf("ps-porep-%d-%d", miner, sector). All ProofShare challenges targeted the same benchmark sector (miner=1000, sector=1), so every concurrent task produced the identical job_id. The CuZK engine's partition assembler, keyed on job_id, mixed partition results from different proofs—producing invalid assemblies that failed verification.

The Fix: Adding Task ID to the RequestId

The fix was elegantly simple: add the harmony task ID to the format string, making it fmt.Sprintf("ps-porep-%d-%d-%d", miner, sector, taskID). This matched the pattern already used by the Snap computation path.

The assistant implemented the change across three sequential edits in tasks/proofshare/task_prove.go, threading the taskID parameter through the computePoRep function call chain. Each edit fixed a compilation error revealed by the Go language server—a classic statically-typed language workflow where changing a function's interface ripples through all callers.---

Part III: The Deployment — Docker Build Cache and the Phantom Binary

The First Attempt: Native Build Fails

The user's instruction was straightforward: "build curio and send updated binary to the vast host." The assistant's first attempt was a native make curio build on the development machine, which succeeded and produced a 163 MB binary. That binary was uploaded via SCP to the remote vast host at 141.195.21.72. But when the assistant tried to verify it with curio --version, it failed with:

error while loading shared libraries: libconfig++.so.15: cannot open shared object file

The Curio binary, when built with CUDA and supraseal support, links against shared libraries that exist only inside the Docker build environment. The native build produced a binary that was dynamically linked against libraries not present on the target system. The user clarified: "it is also a curio node, maybe you need to docker-build (Dockerfile.cuzk) and send the binary from docker build."

The Second Attempt: Docker Build Cache Strikes

The assistant pivoted to building inside the Docker CUDA environment. Rather than rebuilding the entire Docker image from scratch (which could take 30+ minutes), the assistant used a surgical approach: create a container from the cached curio-builder:latest image, copy the three modified source files into it via docker cp, and rebuild using --volumes-from to mount the container's filesystem.

The build succeeded, producing a binary tagged with _psfix2. The assistant deployed it to the remote host, killed the old process, started the new one, and verified the version string. Everything looked correct.

Then the user reported: cuzk logs still showed ps-porep-1000-1—the old format without the task ID. The fix had apparently not taken effect.

The 150-Second Uptime That Exposed the Truth

The assistant's first instinct was to verify the binary directly: strings /usr/local/bin/curio | grep "ps-porep-". But strings (from binutils) wasn't installed on the minimal production image. The assistant was stuck—unable to confirm whether the binary contained the fix.

The user cut through this uncertainty with surgical precision. Instead of fighting with missing tools, they queried the running application's own Prometheus metrics endpoint:

curio_harmonytask_uptime{version="1.27.3-rc2+mainnet+git_96f6c783_2026-03-11T16:08:33+01:00_psfix2"} 150

The value 150 meant the process had been running for only 150 seconds—2.5 minutes. The assistant had deployed the _psfix2 binary approximately 10 minutes earlier. Something had restarted the process after the initial deployment, or the initial deployment had never taken effect.

The implication was stark: the _psfix2 binary that the assistant carefully built and deployed was not the binary currently serving requests. The version string was a lie—a linker flag stamped onto an old binary. The Docker build cache had prevented the source changes from being compiled. Go's build cache is keyed on file content hashes, and the --volumes-from approach had not triggered cache invalidation.

The Breakthrough: Bind Mounts Force a Full Recompile

The assistant's reasoning at this point was a masterclass in systematic debugging. It generated two hypotheses: either the Go build cache hadn't recompiled task_prove.go with the change, or the docker cp into the volumes-from container hadn't worked as expected. To test these, the assistant reached for grep -ao on the remote binary—a pragmatic substitute for the unavailable strings command. The regex ps-porep-%d-%d[^\\"]* searched the ELF binary for the format string pattern. The output was devastatingly clear: ps-porep-%d-%d with only two format specifiers. The fix had not been compiled in.

The root cause was a subtle Docker semantics issue. The --volumes-from flag only shares volumes explicitly declared in the Dockerfile via VOLUME directives—it does not share the container's entire writable filesystem. The curio-builder image likely had no VOLUME directive for /build, so the files copied via docker cp were invisible to the build container. The Go compiler, seeing no changes to the source files it could access, served up cached compiled objects from the image layer. The version string _psfix2 reflected the build invocation flags, not the code content—a critical lesson that version strings are not reliable indicators of whether source changes were actually compiled.

The breakthrough came when the assistant switched to direct bind mounts using -v. Bind mounts override files at the kernel level, ensuring the build process sees the modified source files. This forced Go to detect the changes, invalidate the build cache for the affected packages, and recompile them. The resulting binary (curio-psfix3) contained the correct ps-porep-%d-%d-%d format string, confirmed by inline grep in the build script.

The Silent Deployment Failure

With the correct binary built and uploaded to /tmp/curio-psfix3 on the remote host, the assistant attempted to deploy it using a chained command: kill $(pgrep -f "curio run") 2>/dev/null; sleep 2; mv /tmp/curio-psfix3 /usr/local/bin/curio && .... This command produced no output—a silent failure that the assistant initially missed.

The debugging revealed the truth: the psfix3 file was still sitting in /tmp, untouched. The kill + mv chain had failed. The assistant's hypothesis—"The kill+mv must have failed because curio respawned or something"—was close but not quite right. The actual mechanism is more subtle. The kill signal is asynchronous; it sends SIGTERM and returns immediately without waiting for the process to actually exit. The sleep 2 provided a brief window, but if the process took longer to terminate (due to cleanup handlers, goroutine shutdown, or GPU resource release), the mv executed while the old process was still alive. The running process held a memory-mapped reference to the binary's inode, preventing replacement. The 2>/dev/null redirection on the kill hid any error messages, and the && chain silently aborted on the first failure.

The assistant's response was a model of operational maturity: abandon the clever chained command in favor of boring, step-by-step execution. The step-by-step approach killed the process explicitly by PID, waited three seconds, verified the process was gone with pgrep, and only then proceeded to copy the binary. This separation ensured each step's result was visible and verifiable. The subsequent deployment succeeded, and the new binary (psfix3) was confirmed running with matching hashes and the correct version string.

Part IV: The Proactive Audit — Ruling Out the Same Bug Across the Codebase

At the moment of success—when the assistant reported the fix was running and the user could reasonably declare victory—the user instead asked a question that reveals the difference between fixing a bug and understanding a vulnerability class: "Do we have the same issue in other callers to proofshare? Snap, window/winning PoSt?"

This question was grounded in a specific mental model of how the system failed. The root cause was an identity collision: two logically distinct units of work were assigned the same identifier, causing the downstream system to treat them as the same unit. Once the user internalized this failure mode, the natural next step was to ask where else this pattern appears.

The assistant executed a systematic audit, grepping for RequestId: across the codebase and finding six matches across two files (task_prove.go and cuzk_funcs.go). Each caller was analyzed for uniqueness guarantees. The results were reassuring:

Part V: Consolidation and the Final Docker Image

The Amend: Uniting All Fixes in One Commit

The user's "Success!!" message marked the transition from investigation to consolidation. The instruction was precise: "Commit the cuzk/proofsvc changes (one commit)." The assistant executed a git add and git commit with a multi-paragraph message describing all three bugs—the deadlock, the job ID collision, and the queue cleanup—each with its own root cause, symptom, and fix.

But the user noticed something the assistant had missed. Two modified files remained unstaged: extern/cuzk/cuzk-core/src/engine.rs (the self-check gating fix that made the cuzk engine return Failed instead of Completed when proofs failed validation) and lib/proof/porep_vproof_test.go (test infrastructure for PoRep vproof round-trips). The user's question—"Why not commit the two remaining modified files?"—prompted a discussion about commit boundaries.

The assistant's reasoning revealed a thoughtful judgment: the engine.rs fix was about proof integrity within the cuzk engine itself, while the proofshare fixes were about the distributed coordination layer. In many codebases, these would indeed be separate commits. But the user's answer was succinct: "amend." The assistant performed due diligence first—checking the git author configuration and confirming the commit hadn't been pushed—then executed git add and git commit --amend --no-edit. The result was commit 44429bb7, spanning five files with 822 insertions and 208 deletions—a far more accurate representation of the full scope of work.

This consolidation was not merely a matter of convenience. It preserved the narrative coherence of the changes, ensuring that future engineers reading git log would see the full story in one entry. A single commit that says "fix proofshare deadlock, job ID collision, queue cleanup, cuzk self-check enforcement, and test infrastructure" tells a much richer story than five separate commits scattered across the history.

The Final Artifact: Building and Pushing the Docker Image

With the consolidated commit in place, the user issued the instruction that would cap the entire debugging marathon: "build/push new docker image." This was not a casual request. It represented a deliberate transition from ad-hoc patching—the fragile workflow of building binaries locally, SCP-ing them to remote hosts, and hot-swapping running processes—to a proper, reproducible deployment artifact.

The Docker build itself took several minutes, involving apt-get installations of runtime dependencies and the multi-stage compilation of the Curio binary, the cuzk GPU proving engine, and supporting tools. Before pushing, the assistant performed a critical verification step: running a container with the entrypoint overridden to check that the binary inside contained the correct format string. The output confirmed ps-porep-%d-%d-%d—three format specifiers, including the task ID. The fix was genuinely compiled into the image.

The push uploaded 19 Docker image layers to Docker Hub, all showing "Preparing" status—indicating a completely fresh push, not an incremental update. Each of those 19 layers represented a battle won: against deadlocks, against cache invalidation, against subtle identifier collisions, against the inherent complexity of distributed proving systems. After this push, any instance pulling theuser/curio-cuzk:latest would automatically receive all five fixes.

Part VI: Themes and Lessons

1. Distributed deadlocks require understanding the full feedback loop

The HTTP 429 deadlock was not a simple timeout bug. It was a systemic feedback loop where the retry mechanism blocked the very process that would have resolved the rate limit. Fixing it required understanding the interaction between three components: the HTTP client, the polling loop, and the database queue. The fix's elegance lay in moving the retry responsibility from the low-level client (which had no context about system progress) to the high-level polling loop (which did). This is a recurring pattern in distributed systems: the component with the most context should make the most critical decisions.

2. Unique identifiers are not optional in concurrent pipelines

The job ID collision bug is a cautionary tale about derived identifiers. The system used minerid-sectorid as a job key, implicitly assuming uniqueness. This assumption held for normal proving (where each sector is unique) but catastrophically failed for ProofShare challenges (which all target the same bench sector). The fix—adding a truly unique component (task ID)—is a pattern that should be applied proactively in any concurrent pipeline. The lesson is simple but profound: never assume your identifiers are unique unless you have explicitly guaranteed it, and always audit sibling code paths for the same vulnerability after fixing a concurrency bug.

3. Version strings are not proof of deployment

The _psfix2 version string was present in the binary, but the code changes were not. This is because Go's -ldflags -X mechanism injects version strings at link time without requiring recompilation of the changed source files. The lesson: binary verification requires checking the actual compiled code, not metadata. The user's Prometheus metrics query was the right approach—ask the running process itself. The assistant's grep -ao on the ELF binary was another effective technique. Both are superior to trusting version strings.

4. Docker build caching is a double-edged sword

Docker's layer caching is essential for fast builds, but it can silently defeat production patches. The --volumes-from approach preserved the old Go build cache, preventing recompilation. Switching to bind mounts forced cache invalidation. Understanding when the cache helps and when it hurts is critical for patching production systems. More broadly, the lesson is that any caching layer—whether it's Docker layer caching, Go build caching, or HTTP response caching—can mask the presence of changes and lead to phantom deployments. The only defense is explicit, end-to-end verification.

5. Controlled experiments isolate concurrency bugs

The user's max_tasks=1 experiment is a textbook debugging technique: eliminate the variable you suspect (concurrency) and observe whether the symptom disappears. This single experiment transformed the investigation from open-ended speculation to targeted fix. It is a reminder that in complex distributed systems, the most powerful debugging tool is often not a debugger at all, but the ability to systematically vary one parameter at a time and observe the result.

6. The deployment pipeline is an active participant in correctness

The silent deployment failure—where a chained kill + mv command failed because the process was still running—demonstrates that the deployment pipeline is not a neutral conduit. It is an active participant in the correctness of the system, and it can betray you in ways that no amount of version-string checking will catch. The assistant's shift from clever chained commands to boring step-by-step execution is a lesson in operational maturity: in production, predictability and verifiability are more important than cleverness.

Conclusion

This segment of the opencode session tells the story of two production bugs that, on the surface, appeared unrelated—a deadlock in the request pipeline and a proof corruption in the GPU proving engine. But they shared a deeper connection: both were concurrency bugs that only manifested under the specific conditions of a distributed proving system operating at full capacity. The deadlock was a serialization problem (one task blocking forever), while the job ID collision was a parallelism problem (multiple tasks using the same identifier). Together, they illustrate the full spectrum of concurrency hazards in distributed systems.

The assistant's debugging methodology—systematic hypothesis elimination, controlled experimentation, binary-level verification, and cross-component auditing—is a model for anyone working on complex distributed systems. And the user's interventions—the max_tasks=1 experiment, the Prometheus metrics query, the guidance on Docker build environment, and the insistence on auditing sibling callers—demonstrate the irreplaceable value of domain expertise in production debugging.

The final commit consolidated fixes across five files with 822 insertions and 208 deletions. The Docker image was pushed to Docker Hub. The production system was running with both bugs fixed. But the real artifact of this session is not the code—it is the documented reasoning chain that future engineers can follow to understand why these bugs happened, how they were found, and what patterns to watch for in the future.

In the end, what began as a "partition 0 already inserted" panic in a Rust GPU engine was resolved through Go format strings, Docker volume semantics, SSH process management, binary grep patterns, git amend operations, and a Docker push. It is a reminder that in distributed systems engineering, the simplest fixes are often the hardest to deploy correctly, and the most important decisions are often encoded not in elaborate design documents, but in the careful, step-by-step execution of commands that transform a broken system into a working one.---

References

[1] "Two Bugs, One Pipeline: Debugging a Distributed Proving System's Deadlock and Job ID Collision" — Chunk 0 article covering the deadlock and job ID collision analysis.

[2] "From Build Cache to Docker Push: The Full Arc of a Production Fix in a Distributed GPU Proving System" — Chunk 1 article covering the deployment saga, audit, and final image push.

[14] "The Art of Refinement: How One Question Rescued a Deadlock Fix from Its Own Blind Spots" — Analysis of the user's intervention that added progress-based backoff to the deadlock fix.

[30] "Breaking a Deadlock: How a 429 Retry Loop Paralyzed a Distributed Proving System" — Comprehensive analysis of the deadlock fix summary message.

[46] "Building a Patching Pipeline: Docker Layer Cache Tricks for GPU-Proving Binaries" — The Docker rebuild strategy using --volumes-from.

[54] "The All-Partitions-Invalid Message: When Benchmark Success Masks a Deeper Pipeline Bug" — Analysis of the user's report that all ten partitions were invalid.

[74] "The Smoking Gun: How a Job ID Collision Caused Silent Proof Corruption in a Distributed Proving System" — Analysis of the user's breakthrough message with the max_tasks=1 experiment.

[75] "The Case of the Colliding Job IDs: Debugging a Distributed Proving Pipeline" — The assistant's diagnosis of the job ID collision.

[92] "The Status Update That Sealed a Debugging Marathon: Understanding the ProofShare Job ID Collision Fix" — Analysis of the final todo list update.

[95] "The Moment a Fix Unravels: Debugging the Docker Build Cache in a Distributed Proving System" — Analysis of the Docker build cache failure.

[97] "The 150-Second Uptime: A Single Metric That Exposed a Phantom Build" — Analysis of the Prometheus metrics that revealed the deployment failure.